{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# help(plt.subplots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plt.subplots\n",
    "Create a figure and a set of subplots.   \n",
    "This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in as single call.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters\n",
    "-------------------\n",
    "- subplot_kw: dict, optional  \n",
    "    Dict with keywords passed to the :meth: `matplotlib.figure.Figure.add_subplot` call used to create each subplot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Returns:\n",
    "----------------------\n",
    "- fig: `class` matplotlib.figure.Figure object.   \n",
    "- ax: Axes object or array of Axes objects.   \n",
    "    ax can be either a single :class: `matplotlib.axes.Axes` object or an array of Axes objects if more than one subplot was created. The dimensions of the resulting array can be controlled with the squeeze keyword."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example\n",
    "---------------------\n",
    "First create some toy data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0,2*np.pi,400) # 400个数据\n",
    "y = np.sin(x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Simple plot')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x216 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create just a figure and only one subplot\n",
    "fig, ax = plt.subplots()\n",
    "fig = plt.figure(figsize=(5,3))\n",
    "\n",
    "ax.plot(x, y)\n",
    "ax.set_title('Simple plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xa11d208>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates two subplots and unpacks the output array immediately\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
    "f = plt.figure(figsize=(15,10))\n",
    "\n",
    "ax1.plot(x, y)\n",
    "ax1.set_title('Sharing Y axis')\n",
    "ax2.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xa946fd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8+kayUxLBPDqbG8jmTcG0EsXgWI50fd0UP0ObpUYwNJ4L1CQGQsblJwyj2fyUjb2tKVwjGMl6zmI7jcAYkwOuBx4CngfuM8bsEpFbReRS36lXAveaqZLydGCbiDwFPAJ8yR9tpChzjeM+fNQYw3e2vsEZyzrKdhL7WbfU6RXw0oFBTlgQfgefBE+oRPkIvDo/x4YzkecV0z+aDdQIugpJZZnYUNiBsey0sFsvlDTqzj6Xn2AsO1FIDgMK/oJQU4+7sXtRQ87z+tiooSR5BMaYB4EHi977u6LXtwSM+w1wpvVEijLLOe41gp17+3h+/wBXXbCqbCexn1OXOj0MKukw9kw+XgZxEIvcOj9JykxkchMMjecCfQSeqcwuOzhHR/NUYWFjGhoeD97UnWMhd/jj0009belUJZ3FiqK4HPeC4O7H36C1McVlZwdGBpbMojan01Y5rSOL8cJCo0xDnkYQ163Lj1fnpyvIWexqBDaCwKn9M/UaVlVBM9OdzHECxNvY/cKjpbE+1IQ16s6hJSYUJTnHtSA4NDjOlqf28cFzlsf2JC6FU5a0VVQjODA4TkNKIrtseQlhXu9gG7yErwWBpiFnY7fJLh4YzdJRVObZ8xFEaQReZJDf9BSnEQwHOH9bGlMFp3AxkxrBcW/tVJSKc1wLgm/+5jWy+Qmu+b2TqnL9dUva2H1wqGKRQwcGxljS3kRdQFaxx8ISTEN9ERrBpGko/nqDY9kp5SUA0vVOT4KBCB+BJySmCgK3ZEScRjBFEIRrBCPJw0cVRXE5bgXBSCbHt7e+znvOWMraRa3xA0pg7aJWBsZy1nH4cRwcGC/0AQ6jo6mB+jrhSIJew4V+wc0B4aPN9qah4uxgj/am+sh8AG+z92tl6foUDSkJdf56Y1r8foXGVMF3UMxoNk9TQ11gaQ5FUaI5bgXBfU/upX80y7W/H5jwWRFOWuwImD2HK+MnODAwFukoBqirE7pbGxP5CI75msAX01hfR3NDyqq5zODY1HpBHlFhnTCpERSb56LzAgI0gnQ9I9k8ExPTNbARX4E6RVGScVwKgkxugq/9ag/nru7i3NVdVZtn7SInhHTP4dLbR/o5MDAW6Sj2WNiWTmQa8tpEhjV2X9Ac32Usk3NCQNsDfC1xJaXDBEFbhCAoaASNfmdxCmNgLDddixjJ5NUspCglclwKgu9u20vPsVGuv+iUqs6zoquZVJ1URCMYzeQZGMtZ5QYsbG1M5Cw+NpKh3m35GISNIPDyBAI1grSdaag4TyGqOc1k1JDPr+AKhSA/wWgmWS8CRVEmOe4EwWgmz1d+9jK/s6aLd5xW3SJfDak6VnW3sOfwcNnXOjjohY5aCIK2Ro4kdBZ3tjSE5lHYCYLpIaAebemGSGfxpCCYulG3putD+wtMRg1NDR8Fp2FNMSMqCBSlZI47QfDNx17j4OA4N1y8vqIJZGGsXdTKq4cqIQjiy0t4LGxNJ3QWZ6Y0hSmmw0IQTLaanH6djhgfweB4jsZU3bQS2FGZwiPjeVJ1Qrp+8ifqbfRBwkPbVCpK6RxXguDN/jG+8rOXeef6JZy3trsmc65d1MrrR0YCHZhJmEwms9MIhjN5xrLx9YHArTMUkZuwoLmhUFAuDE8jKM4jgPhuY07BuembdFs6xVCIJuF0J0tNEeZeJdIg09BIVp3FilIqx5Ug+MKPnyM7Ybj5A2fUbM61i1oZzeY5MJi8Iqgfr85QXNQQTOYSHLGMHOobyQbmEHgk8REEaQTtTdFNZobH84XEMz+tjfWF8hPFjIznp0QMOed7PoLpQmdENQJFKZnjRhD85Jn9/Ojp/XzyHSezemF18gaC8HIU9pRpHjo4MEa6vm5aLZ8gvDITtuYhxzQUrREMZ/KRFU0HRl2NIGB9bemGQrXQIAbHctM2dYgOHx3O5KbkEMCkjyBIeIxm8rRo1JCilMRxIQj2949y4wPPcNaKBXzyHdWNFCrGEwSvlukwdjqTpa38GoUyE5YO477ROI3ALQkdoRVE+QjiSlEPj+cCI5baXGdxkCbhdCebOqYlRiNQZ7GilMacFwTD4zk+8a1tZPMT3P6Rs2msr+1HOqGjiaaGurIjhw4MjFuZhcBfZiJeIxjP5RnJ5ENzCICC/yDKPORt8kEbekdMvaHi7mQerWmnXWWQJuH0Ky7SCNLR4aMJGtcriuJjTguC0UyeT979W57bN8AdHz2Hkxa31XwNdXXCmoWtvH6kTEEwOGblKIZkFUgnk8miTUMQLQgGxrK0p+sDSzgUKomGaARDoRpBqnC8mJFMfprw8DSEYo0gl58gk59QjUBRSqQmgkBELhGRF0Vkt4jcWIlrvtk/xlVf28ovXj7EFy8/k4vWL6nEZUtizcLWsjUCmzpDHq2NKRrr66wEgVdwLkoj6LAQBIMhdYYgvqR0mGlosgJpgEaQma4ReJnDxed73clKEQRxv00R+biIHBKRne7jT33HPiYiL7uPjyWeXFFmCVXXpUUkBdwBvBunYfiTIrKllNZ+ExOGN46O8MOn9nHnL18lm5/gX686h0veuqzSy07E6kUt/PyFg+QnTElFz4bGcwyN56w1AhFhYWujVZmJY66wCCo452GlEYwG1xkCGx/B9Lt7iC5FHRQ1VFcnNDekppmSCo3rEwqCBL/N7xpjri8a2w3cDGwEDLDdHXss0SIUZRZQC6PqecBuY8yrACJyL3AZCZt937dtLzc98Ax5N17/D9Yv4f96/xmsqVJl0SSsXdhKJj/Bvr5RVna3JB5/0KIhTTFO4bl4H4GNRmAjCKI0gva0Mz5II5iYMAxncgUzkJ8oTSIoagic7ORiwVFGd7JyfpsXAw8bY466Yx8GLgHuSboIRZlpaiEIlgN7fa97gPP9J4jItcC1AKtWBTeYP/2EDq77bydxYmczF568aFYIAA8vXPW1I8MlCYIkOQQeC9vSdqahiMqjHgVBEFGKenA8y5KQ9RWa0wQkh41k8xgzvc4QhGsExpjAqCEI7kng+QyaGxL/nGN/my4fEpHfB14C/soYszdk7LQ2eDa/bUWZaWrhIwiylUyJFzTG3GmM2WiM2bh4cXB9oDNXLOCGi9dz1fmrZ5UQgMkQ0tdK9BN4dYaSNKN3Cs/ZCAKvKU24acimFPXAaC4wqxgmawgF3tmHFJyDcGfxeG6C/IQJ1AhaGqdrBKU0rneJ/W0CPwTWGGPOAn4KfDPBWKvftqLMNLUQBD3ASt/rFcC+GsxbM5Z2pGlqqOO1I6WVo7bpVVxMd6td4bljI1kaUhK7ScZlFw+OZQNzCMDtUlZfx2CAIAgrQQ3hzuKgXgQeLY3TfQRlmIZif5vGmCPGGM8G91XgXNuxijJXqIUgeBJYJyJrRaQRuALYUoN5a4aIE0JaqkZwYGCclsZUor7K3a2NjGbzhbvhMPpHM3S2NMYmqkUJAmMMA2O5yKzn9pBS1FEaQZhpKKgXgX9MmI+ghBITsb9NEfFHIlwKPO8+fwh4j4h0iUgX8B73PUWZc1TdR2CMyYnI9Th/JCngLmPMrmrPW2vWLGzl5YOlNbJ3GtI0JaqWOllvaJwVjeF+iWPDWTojKo96RAmC0Wye/IQJ1QggvEvZUEgJapi84y8e593xBwmP5oYUhwbHi86f3ujehrDfpojcCmwzxmwB/kJELgVywFHg4+7YoyLyeRxhAnCr5zhWlLlGTVIxjTEPAg/WYq6ZopwQ0oMD4yxptzcLwdSkshVd4YKgbzQT6R/w6GhuoOdYsGkrqheBR1hzGs/sE6TtpNxw0DCNIOgOvzU93VnszdFaQh5B0G/TGPN3vuc3ATeFjL0LuCvxpIoyy5jTmcWzCX8IaVKSZBV7dFtWIO0bybIgImLII6oUtfd+R5RGkK4P9BFEmYa894v7C8T5CIozi0vNI1AUxUEFQYXwIpleS1hqwhjjFJxLqhG02hWeiytB7RFlGhqw0Ajam4I1As/sE9TrGNyeBEXO4qFYH0GYs1hrDSlKKaggqBBrFpYWQto/mmUsO8EJCxJqBG4F0riksj7XWRxHVCnqgYh+xR5h/YetNIJpzt/wSCMvs9jfCGgkm6Oxvq6krG5FUVQQVIylHWmaG1KJQ0jfdENHkwqC9nQ9jam6SNPQWDbPWHYiMpnMI6oUdVR3ssJ6mhpCBYFIeGhna4AA8TSEsMximFqxVBvXK0p5qCCoECLC6oUtiTWCN/tdQZDQRyAiTpmJCNOQl0wWVWfIw/MjBJmHPOEQGzUUaBpyMoTDIqLaAjSC4Yjcg0JzGp+fYESb0ihKWaggqCBrFrayJ6GPIEmv4mK6Y7KLj7nlJWx9BBAiCFzT0IIY01AmP8F4rjiiJ7hfsUegacjVIpoDNvdCc5rxqRqBOooVpXRUEFSQNYta2Xt0pFAYz4b9/aULgoVt0YLA0whso4YgTCPI0ZiqIx3R9Ke9UG+oyMwT0pTGI9hZHK5FtBR6EkyOGclo43pFKQcVBBVkzcIWsnmTKIT0wMAYi9oaS+qsFleBtK+gEdg5iyFcI+hoDjfvwKQZp7gU9dBYcC8CD6eB/XRncbhPYXq7Sm1cryjloYKggnghpEma1LzZnzyHwGNhazraR2BRgtrDiwgKchZH9SLwCCspPTwe3LjeozVdX8hc9gjraAaTpqFhn0YwmlVnsaKUgwqCCuJVIU3StvLNgfHEjmKPhW2NDGfyjAX0/AW/j6A8jaB/NBuZTAbhzWmGxuNMQyHO3xC/QsE0NF50vgoCRSkZFQQVZEm7E0K657B9COmb/aMsTRg66hGXXdw/kiVdX0eTRURNuj5FU0NdiGkoF6sRhDWnGc6EN7SBSQHiNw8NRWgRrQE+Aid8VH0EilIqKggqiBdCaqsRjGXzHBvJlqwReIIgzDx0bMSuzpBHZ3NjwcHsZ3A0G5lDAL7mNONTxzttKqOjhpzzJgXBcIQW0RLgIxjO5EqqM6QoioMKggqzdpF9COlBtzNZ0mQyj0VtkxVIg+gbyVr5BzzCykw4zmJLH0Fi05DXnMYfBRTc4xiCfQTD4zlaEpTwVhRlKioIKszqhU4IaS6gVEMxhazikjWCyQqkQVRCEBhj6B/NRuYQwGT4qL/wXDY/QSY3QVuUs7gx2DQU1OMYoKk+hcikaSiTmyCbN4l6OSiKMhUVBBVm7SInhNTLD4hif78TZlqqRtAdU3ju8PB4oVy1DR0BgmAs62y0cc7idH0d9XUyRSOIqzPkP+b3LYyMh+cF1NUJLQ2pgrPYMxGps1hRSkcFQYXxGtnbhJD2HHMEwfLO5pLm6miqpyEloc7iI0MZFrXa+wiCSlFPFpyLvuMWkWnNaaLaVHq0FfkIJiYMwxGmIYDmxvqCaWg4omR1HCJyiYi8KCK7ReTGgOOfEZHnRORpEfmZiKz2HcuLyE73cVx13FPmH6pPV5ipIaTRzcp7+0bpbm2M3PSiEBG6WoKTyjK5CfpHsyxKoBEEmYZsehF4FDenGUqgEXiCoNCdLOIOvzU92ZOg0NYywiEdhIikgDuAd+P0H35SRLYYY57znbYD2GiMGRGRPwP+EfiIe2zUGHN2okkVZZaiGkGFSRJC2nNstGRtwGNhWzrQR+C9l8Q0FFSK2qYEtUdxc5qB0VzhulFjYNJZbGNOammc7FJWOD+5RnAesNsY86oxJgPcC1zmP8EY84gxxvuP3IrToF5RjjtUEFSYQhVSi8ihnmMjrOgqUxC0NnI4wEdweMjREha2JTENTS9F7WkIcc5icLQGv0ZQ0CYizEpNDXXUyeSGXjD1RIWc+rqUFbqZJdeqlgN7fa973PfCuAb4iX/pIrJNRLaKyKawQSJyrXvetkOHDiVdo6LUBBUEVWDtotZYQWCMoffYaNmCYHF7elozd5gUBIlMQwGlqL27+rg8ApjewN5GiIjIlJ4ENnf4zY2pQpeyqG5mMQQVTgqsFigiVwMbgdt8b68yxmwEPgrcLiInB401xtxpjNlojNm4eHG0qVBRZgoVBFXAJoT08FCG8dxEZON5G5Z2NHFwcGxKxy6YjCRalEgjmC4I+kftTUPtTfVThciYnX/B35PAyq/QWF/oU+xpBiVoBD3ASt/rFcC+4pNE5F3AZ4FLjTEFiWuM2ef++yrwKLAh6QIUZbaggqAKeCFACVHdAAAgAElEQVSk+/rCQ0h7jjmm53J9BCd0pMnmDUdHppqHJk1DyXwEMFUQeL6GTgtB0NXSWKh4CpPaRFSJCZjawN5mY29Jpwrne5pBCZnFTwLrRGStiDQCVwBTon9EZAPwv3GEwEHf+10iknafLwIuBPxOZkWZU6ggqAKF/sUR5qFC6GiZpiEvB+HNoryFI8MZ0vV1iTbIMEHQ2dJAfSr+p7KguYGBsVxBE+ofzdKWro8d65iGnA3dK1oXte6WxlTBN1DII0ioERhjcsD1wEPA88B9xphdInKriFzqnnYb0AZ8ryhM9HRgm4g8BTwCfKko2khR5hQaPloF/OWof//UYLuwl2ewemF5pqElblbygYEx3rp8QeH9w0PjLGpLR/YQKCaoFPXR4QzdlvWKvE5oA2M5ulsbndIUNr6FdKpgGvLmjmqm4+9hUOhvXEKrSmPMg8CDRe/9ne/5u0LG/QY4M/GEijJLUY2gCixpT9PRVM9LBwZDz3n10BAnLmgqu2qmV57CK1fhcXgok8g/AOEaQbdlUlqnKzC88tf9Fn0MYOrGXuiqFjGupbGe8dwE+Qnj1BlqTFFXZy/wFEWZigqCKiAirF/WwfP7B0LPefXwMCctbit7rsXtaUTgQJFp6NDgeKKIIQguRZ1MEDibt7eZ2zS0ATcRzRUE/aNZmhtSpOujE8rAMQvZ1EFSFCUaFQRV4oxlHbz45uC0aB5wQkdfPTTMSYtby56nIVXHorb0NI1gf/8oyzpL6IPcmp5Su+hICRqB5zAeGMtZZST7G9j3jcYXyvPaUo5k8vSNqCBQlHJRQVAl1p/QznAmz95j0zOMDw2OMzSe46RF5QsCcMxDbw5M5hKMuhvksgXJHdFLOtIcGHSEysSE4diIvSDoKtIIjrmO5jhaizSCuI3d35xmwEJwKIoSjQqCKnH6sg6AQPPQK4ccR3ElTEPg5BL4TUP73KqmJ5agESxtbyr0SRgcy5GfMPYaQfOkj8AYw5HhcRa3x5un2tIpsnnDeC5Pv8UdvleWYmA0S99oRjUCRSkTFQRV4tSl7dQJ7NoXJAiGACpiGgJY0dXM3mMjGOOYofa7+QulaARLO9IccM1MB13NwGYzBydfoE4cQdA/miWbN1Z+ikmTUpZ+izv8btcJfnQ44/RcaE7mFFcUZSoqCKpEc2OK05d18Ns3jk07tmvfAB1N9WUnk3msWdjCSCbPITeJbF9f6eWtl3Q0MTCWYyybZ5+rZZxoeZ26OmFxe5qDA+OFshc2QsQ759DguNUd/iK3Ic/hoXErn4KiKNGoIKgi567uYscbfdNKTeza189bly9IFOMfxWrX1/DGEccf0dM3Sp04JqOkLHE35YMD4wWBsixB45wTFjTz5sBYQSgtttAI/ILA0Qii7/C9Qnr7+sbI5CasIpMURQlHBUEVOXd1FyOZPC+8OZlPkM1P8ML+Qc70JX+Vy2QmsyMIXjk0xMruFhrrk//3LvHlJezvG0USCpQTOtK82T/m0wjizTae8Hnj6Ahj2YlYjaCl0Qlz9UxsqhEoSnmoIKgiG9d0A7DttaOF957p7SeTn+CsFZ0Vm2dFVzOpOmHPYWdj3H1giFNKdESv6nYynV87Msy+/jGWtjfRYFFewmPZgmbe7B8rlMZe3BYvRDw/wrO9/cCkYAhDRFjYmp4UBOojUJSyUEFQRU5c0MSKrmZ++fLhwnuPvXIEgAtO6q7YPA2pOk5e3MqufQPk8hPsOTzMKUtKEwQru5ppTDl326XkIpywoInB8RyvHR6mMVUX2+ISoKkhRUdTPc+4gsCmh/PCtkZePqgagaJUAhUEVUREeNfpS/nl7sOFhKlfvnyI05d1JKoKasPbVnTydE8/bxwdIZOf4OQSBUF9qo61i1p55eAQrx0eKWgItnj+hMf3HGFFV7O1H2Rxe7pgQrMxRS1sbSSTc3wvts5sRVGCqaogEJEPi8guEZkQkY3VnGu2cslbTyCTm+DHT++n59gIj+85yrvPWFrxec5a2cnR4QxbnnJK6r+tDNPTyUta2fFGH719o5x2QnuisStdwfHSgaFC/2YbvM1cBFZa9GhY0j4pLJI4sxVFmU61NYJngcuBX1R5nlnL+Wu7WX9CO//2i1f4f3++G4APn1v51rfnr3VMTbf/9GUWtTWyrkSNAJzyGEfcPgRJBcp6n+BIIkS8ccs6mgolJKJYt9T5fKk6oamEyqOKokxSVUFgjHneGPNiNeeY7YgIf3vJevYcHubeJ/dy5XmrCnfNleTUpe1sWOVs2h/euLKsapwXv+UEwNlkN67pSjS2pbGec9x1vHP9Eutxv3vKIgA2rLKb7zxX8F3irlVRlNIRLxu1qpOIPAr8jTFmW8jxa4FrAVatWnXu66+/XvU11Zptrx2l59go7ztrWaIonCQcHc6w9dUjvOv0pSWFjvp57JUjLG5v5JQlyUxD4DTJ2XN4mLefvNB6jDGGR188xO+s7S6UkIjj6Z4+VnW3xOYdFCMi291+wzVl48aNZtu2wD8BRSmbcn7XZQsCEfkpEHRb9lljzA/ccx4lQhD40T8Wpdr4/2BE5BLg/wFSwNeMMV8qOjcNfAs4FzgCfMQY85p77CbgGiAP/IUx5qGoefW3rVSTcgRB2R3Kwro4KcpsR0RSwB3Au3Ga2T8pIluK2k5eAxwzxpwiIlcA/wB8RETOwOlz/BbgROCnInKqMSZf20+hKOWj4aPKfOY8YLcx5lVjTAa4F7is6JzLgG+6z+8H/kCcmNjLgHuNMePGmD3Abvd6ijLnqGrPYhH5IPAVYDHwYxHZaYy5OGrM9u3bD4tImJNgEXA45NhsRNdbXUpd72r33+XAXt/7PcD5RecWzjHG5ESkH1jovr+1aOzy4on8/i9gXESeLWG9lWCm/m/n27wzOfdppQ6sqiAwxvwn8J8JxwR3ewdEZNtMOPlKRddbXSqw3qDQqmKnWdg5NmMxxtwJ3Akz+/3O1Nzzbd6ZnFtESnZAqWlImc/0ACt9r1cA+8LOEZF6YAFw1HKsoswJVBAo85kngXUislZEGnGcv1uKztkCfMx9/kfAz40TarcFuEJE0iKyFlgHPFGjdStKRamqaagK3DnTC0iIrre6lLVe1+Z/PfAQTvjoXcaYXSJyK7DNGLMF+DrwbRHZjaMJXOGO3SUi9wHPATngUxYRQzP5/c7U3PNt3pmcu+R5a5JQpiiKosxe1DSkKIoyz1FBoCiKMs+ZM4JARC4RkRdFZLeI3DjT64lCRFaKyCMi8rxbhvsvZ3pNNohISkR2iMiPZnotcYhIp4jcLyIvuN/z22d6TR5xv1XXwfxd9/jjIrKmRvN+RkSeE5GnReRnIrI66DrVmNt33h+JiKlUWXqbeUXkj93PvUtE/qMS89rMLSKr3H1gh/udv7cCc94lIgfD8lHE4Z/dNT0tIudYXdgYM+sfOI68V4CTgEbgKeCMmV5XxHqXAee4z9uBl2bzen3r/gzwH8CPZnotFmv9JvCn7vNGoHOm1+SuJfa3CnwS+Df3+RXAd2s070VAi/v8zyoxr+3c7nntOCXptwIba/SZ1wE7gC739ZIa/j/fCfyZ+/wM4LUKzPv7wDnAsyHH3wv8BCfP5QLgcZvrzhWNwKYUwKzBGLPfGPNb9/kg8DwBWaezCRFZAbwP+NpMryUOEenA+YP4OoAxJmOM6ZvZVRUop2xFVec1xjxijBlxX27FyX2oBLZ/n58H/hEYq+G8nwDuMMYcAzDGHKzh3AbocJ8voAJ5JsaYX+BEr4VxGfAt47AV6BSRZXHXnSuCIKgUwKzeWD1ctX8D8PjMriSW24H/DkzM9EIsOAk4BPy7q3Z/TUTs26FVF5vf6pSyFYBXtqLa8/q5BufOsRLEzi0iG4CVxphKmh1tPvOpwKki8msR2epWm63V3LcAV4tID/Ag8OcVmrvcdU1jrggCq3T+2YaItAHfBz5tjBmY6fWEISLvBw4aY7bP9FosqcdRj//VGLMBGAZmi9+onLIV1Z7XOVHkamAjcFuZc1rNLSJ1wD8Bf12h+azmdanHMQ+9A7gS+JqIlN7HNdncVwLfMMaswDHZfNv9LqpJSb+tuSII5lw6v4g04AiBu40xD8z0emK4ELhURF7DUXHfKSLfmdklRdID9BhjPC3rfhzBMBsop2xFtedFRN4FfBa41BgzXuactnO3A28FHnV/YxcAWyrgMLb9rn9gjMkap0rsiziCoVxs5r4GuA/AGPMY0IRTkK6alLZXVsJxUu0HjlR/FVjLpGPmLTO9roj1Ck4zk9tnei0lrP0dzA1n8S+B09zntwC3zfSa3LXE/laBTzHVWXxfjebdgOPgXFfrz1x0/qNUxlls85kvAb7pPl+EYzZZWKO5fwJ83H1+urshSwXmXkO4s/h9THUWP2F1zUr+IKr5wFGtXnJ/yJ+d6fXErPX3cNSxp4Gd7uO9M70uy7XPFUFwNrDN/Y4340aFzIZH0G8VuBXnLhycO8Pv4fQweAI4qUbz/hQ44PtNbqnVZy46tyKCwPIzC/BlnFIgzwBX1PD/+Qzg166Q2Am8pwJz3gPsB7I4d//XANcB1/k+7x3ump6x/Z61xISiKMo8J9ZHUE4Cg4h8TERedh8fCxqvKIqizCw2zuJv4NjZwvhDHOfLOpxOTP8KICLdwM04HZ/OA24Wka5yFqsoiqJUnlhBYEpPYLgYeNgYc9Q4yRwPEy1QFEVRlBmgEv0IwhIYrBMbxNfXtbW19dz169dXYFmKEsz27dsPm4iWqNVi0aJFZs2aNbWeVpknlPO7roQgKKunK0zt67px40azbVvJrTcVJRYReX0m5l2zZg3621aqRTm/60oklIUlMMy5JDBFUZT5SCU0gi3A9SJyL45juN8Ys19EHgL+3ucgfg9wUwXmU5RZgYjcBXjlOd460+uJYvOOXm743k6yAZWkLjy5m7s/Ubsq3p/b/Azf2fpGRdeweUcvNz3wNKPuB6wT+Oj5q/jCpjMTrevurW8UzBatjSn+1wfPZNOG6FI9m3f0csuWXfSNZgHoamng5g+8JXLc5zY/wz2P7yVvDCkRrjx/ZeRaN+/o5baHXmRf3ygndjZzw8Wnxa4rCTbho/cAjwGniUiPiFwjIteJyHXuKQ/iZNjtBr6KU2IXY8xRnGqDT7qPW933FOV44RvMgQCIzTt6+fR3g4UAwK9fOcr5/+vhmqwlSAh4a3j3lx8t6Zre5xv1fcAJA9/Z+gaf2/xMonX5bdfDmTx//b2n2LyjN3Luz9y3syAEAI6NZLnh/vBx3lx5N4crbwzf2foGV331sdDzP/3dnfT2jWKA3r5RbohZV1JsooauNMYsM8Y0GGNWGGO+boz5N2PMv7nHjTHmU8aYk40xZxpjtvnG3mWMOcV9/HvFVq0oswCLiLpZwd9876nYcw4MZkI3okpyd4AQ8Hj54HBJm9v//OGukubz8x+PB5+XnzDc9tCLkXNPBHg+s/nwcWFz/fqVo9M+/+YdvYGCMzthuGVL+OdOylwpOqcocxIRuVZEtonItkOHDtV8/qu++hi5oJ0qgKCNqJJs3tEbWwYzatMN49hINvSYbd2EqK+ot2+0pLmDxm3e0Rs5V/Hnj/o+/FpIuaggUJQqYoy50xiz0RizcfHi2kasbt7Ry69fSaawVPIusxibTT5q0y2VcoVbqsSeQUHj4r6D4s9fje8jCBUEinKc8rfffzrxmEreZRZjs6kl3XJtNvm4zTfuGvkS67EFjdsX8x0UC48oIdTV0lDSuoJQQaAoxyGf2/wM47nSms1VyzxUZ7HLJ91yK6FlxF0jbDOO+56CxnXGbN7FwiNKCN38gbdEXisJKggUpUSCIupmek0Q7mC0pVrmIUtXRSJBZKNlxJl24q4RthnHCZCgcXHKxfLO5sjXHp3NDbUNH1UUJZigiLqZXhNER9F4pOvD//SrYR5KsrkncRjb2O/jTDtx1xCC1x9n5ulsnn733x/z3V60fnHka4/3vy22H30iVBAoynFGVCQLOMlb//ChsyLPqbR5KMnmnsRBamO/D9qQk1zDELz+E0Pu1j2GM7lp32PcmEdeOBT5Ou79UlFBoCjHEXEJVA11cPcn3h5rVqi0eSju7tlPkigdG4dp3OXCzC9+gtYfdrfuEZRLEDemeJ6w7y3J92mDCgJFOY6IS6C67cNnF55HbaKVNg/FOUn9JInSsTk1TkOK25wh+E7e5q68eMOOG1M8T5gGEadZJEUFgaIcJ8QlbDU31E3RBCoZdRJHkgjMMJt8EHE2d5vr2WzoQcLC5q68eMOOGiMB81y0fvG0kNrmhhQ3XHxa7NxJUEGgKMcJcU7iL14+1S9gU0ytUiTRMMJs8kHYaBpx17PZ0IOERdxdeUOdTNuwo8YY4Pvbewvf++YdvXx/+1ThLsCHzl1e0YghUEGgKMcNUSaQhrrgjT/KPFRJP0HS7FybzXnzjl6GxnJlX89GmASNv+Hi02hIRXyugEM3XHwazQ2p0CGj2XxBaN320IuMZvNTjhsq7ygGFQSKclwQd/fu9w34iTIPVdJPkDQ7d0FMpA84G2XWMjkh7E7cVpiE3slHTB9WeC4qdBcmhU6tHMWggkBRjgviykmEmRJqYR7avKM3tHREmKJgo0Ak2RDDHMK2wiRovM1Y/xqdngnPxApYT+jUylEMKggUZc6zeUdvZDmJuDj6KPNQKdVAg64RtF0K4U7kvphIH0gWiRRmTrEVJkHjkzqLg0w9xfgdwUFmpGo4ikEFgaLMeeKcxLdcGh0dFGUeqkT1y7AN0xAupGxMQ0msTWFrsL27DhofN7Y4Csim4JzfEbxpw3K+ePmZLO9sRnDyHb54eXzHtFKwEgQicomIvCgiu0XkxoDj/yQiO93HSyLS5zuW9x3bUsnFK4oSHycft3FEHS+tAPNUwu7cu1oayjIN2YSOeoRt2rEO34jxcY7f4iigOMGRN2bK+bXEplVlCrgD+EPgDOBKETnDf44x5q+MMWcbY84GvgI84Ds86h0zxlxawbUryrwnbtO4+oJVZV2/tALMRdcIuYgx4SagOOEG4Rtr8bYeFJ8/dSHR80SN9zt+g6qr+qOA4gRH8fmeT8HfovKmB56piqCw0QjOA3YbY141xmSAe4HLIs6/ErinEotTFCWaOLNQkubtYZS78YTdufePZiM387h5g+7mG1LC757cPUUYFN+Z+7Fx+AaND3L8hl3GMwlt2rCcD527PDaU1js/yKfgFxSVxEYQLAf2+l73uO9NQ0RWA2uBn/vebnJb9W0VkU0h42a0nZ+izFWi7pzjnMR+qukwDjMNdbY0cMPFpwWan6yTyoo3XwPP7R+c9nbYBmrrLC4eb+P49fCEnZcgFhdK650/28JHw/6fgrgCuN8Y4/+GVhljNgIfBW4XkZOnXWwG2/kpylwlrsBcnJPYTzUdxlGmoU0bloduJnEbXtDdfHbChArHUhy+YeNtN2N/lE/SqKHZFj7aA6z0vV4B7As59wqKzELGmH3uv68CjwIbEq9SUZRpxBWYSxJdsmnD8tAOYqX27PUIi5v3TEZh1T/jNrykd8ZhDl8bZ3Hx+LC1dTY3hEb5xNUZKj5/toWPPgmsE5G1ItKIs9lPi/4RkdOALpyOTd57XSKSdp8vAi4EnqvEwhVlPhNXYC6JWcgjzMZdas9eiE4m8zbTMEdsXFXQMJNTc0PwthZ6vaKPVwfThEPxBhy2SUc1jAkTHmGCdlaFjxpjcsD1wEPA88B9xphdInKriPijgK4E7jVmyq/mdGCbiDwFPAJ8yRijgkBRyqTc3IEgwjakcvSBqGQyb2MtpflKWGmIhpTQFBKZE3S9IPPSBFBfJ4Xvozi+H6Y7flMinLNqAd/f3hsa5RMWNZQ3JvD8zTt6ue2hF9nXN8qJnc3ccPFpVRECAPU2JxljHgQeLHrv74pe3xIw7jdA+WELiqJMoZQCc3GE3fkbnE2plGtGJZPFmUyiTClh0T6tjfWhIalB1wubYzQ7mantxfdvXN1dWHOx4zdvDL955Wiok3rThklB4m3udSLTvnO/U/qmB54p+BQ8IQGl/d/GoZnFijLHiHMShxWYiyOqU1epkUNRyWQeYVnEUdnFYRt4VEhq0Pu2jlebqKGkTu8wwbuvb7SmoaOggkBR5hyVdBL7iXJClho5FBUx5FFKdnHUZn/DxafRUOT5DuoNAHZJXh6lRA3511qcIBZ1fi1DR0EFgaLMKT63+ZmKO4k9qhE5FJVM5hFmyokqPBfrYA5KLw7Bnx3c1dIQmlNhEzUU1U0sSfhoLUNHQQWBoswZNu/o5Tsx2kApTmI/lY4cikom8wjb3KJMQ1EO5tseepFsvii/IKA3QFB28Fh2gvedtSw2bDMsauh3T+4OdTInCR+tZegoqCBQlDnDZ/8z2jdQqpPYT9idf6kagY1pKMiUAzCcyYWWmYgyndiaVcLs8I+8cGhaRJBt1NBv3+if4kC2KTq3vLOZPV96H7++8Z0zUnkUVBAoypxg845ehjPRZoVSncR+wu78S9UIbExDmzYsp61pegBjWIcviPYR2JpVwgRGb9/otIigoFpDQVFDUQ7esLv8i9Yv5sIv/Zy1N/6YC7/088I8mzYs59c3vnOakKgGKggUZQ4Qpw00N9RVZKMIixyyKQIXRJhpqHhTThLyCeEF5264+LTADTeogmhUgldcxE4pUUNBd/kfOnd5aO7B5h29gQKiGqggUJRZjo028MXLz6rIXGUXgfMRlfRVbOsuyTkaUHAOJs02cRVIw+7Qo8I6g57H4Y8aKk4Qe+SFQ4FC55Ytu2pWghpUECjKrKdW2gBEF4FLGkIalfRVvN6kZSbCCs55wuqRFw7FViANsvN/6NzlVrWPkkYNhfUWCPtO+0azmkegKIpDLbUBj0o5jKOSvopJWmYiziFs4zAOsvN/f3svF61fXHLU0FUXrAp08IY5piv1nZaLVYkJRVFmhr/53lORxyupDXhUymHc2dIQWAojyG+QNIHqxM7mwLtp70497jhERw198fIzuWXLrkJoaVNRIbvichFxtYCisoubG1JT1tHckKKpoS7wu9M8AkWZZ7z7y4+Si+meVWltACqnEdiEjnok9RFctH5xZPKWTRx+nPAZz03WGzo2krUuCBfk5I0KHQ0KE735A2+paR6BagSKMgu56quP8fLB4chzqqENQOU0ApvQUY+L1i8OTJYL8hF4Jh3/agSmxPpv2rCcba8f5Z7H95I3JjAXIEpriKv1E1YQLuyYFx1UfOfvCZCw/8dZVX1UUZTa8bnNz/DrV47GnlcNbQCcu9KgDdILIbXdjGzMMx5JfARhoZv+c8Ps//4KojdcfNqUTRsmN+e/+u7OwPXYFISLMjcFbexh2kWUgKg0ahpSlFnEVV99LLaMBMCFJ3dXbZOoVAhpkkigJD4Cm3NtqneGRQ1t2rA80lRValZzUIJYWDRRNXMGgrASBCJyiYi8KCK7ReTGgOMfF5FDIrLTffyp79jHRORl9/GxSi5eUWaSuL8LGzbv6GXd//gxa250HjaaQB1w9yfeXsp0VlQqhDTJXX4lSkf73y8namjzjt5IH0OpWc1BvoMogTWrEspEJAXcAfwhcAZwpYicEXDqd40xZ7uPr7lju4GbgfOB84CbRaSrYqtXlBkiwd9FKJt39PLp7+7E1wPFii9/pPxSEnFUwmEcJjSCNmnbbGDv3LCsYg8bYRG1CUfV+okSElFlJJLkEfiPz5aEsvOA3caYV40xGeBe4DLL618MPGyMOWqMOQY8DFxS2lIVZVZRzt8FUFqzl6svWFUTu3G5DmObXsV+bLOBpxyMeG3Tk6DUmv9RQiLsWFgGcZTAnW0JZcuBvb7XPe57xXxIRJ4WkftFZGWSsSJyrYhsE5Fthw6F9ylVlFlE2b/tpMlBF57czRc21abza7kagU2v4mJssoG9a0dlFU+ZLOJ1nBmnknfkcXkEfmzLXFQSG0EQ5jfy80NgjTHmLOCnwDcTjMUYc6cxZqMxZuPixcEOJkWZZZT9206SHLRuSWtV/QLFlKsR2PQqth1T/L6tsziuJ0GUiSfOdh8mJMKOhRXfC8sjsClzUUlsBEEPsNL3egWwz3+CMeaIMWbcfflV4FzbsYoyRyn7t22bHHThyd08/Jl3JLl02ZRbhdSmV3Extg7jSjmLYXp3Ms/EEzU+SkiEHTOGUKETFE00GxvTPAmsE5G1ItIIXAFs8Z8gIst8Ly8FnnefPwS8R0S6XCfxe9z3FGWuE/t3EcemDcu5/SNn0xDyV9jamOL2j5xdU03Ao9wQ0iRZxR624aaVcBaHdSezGV9K+Gj/aDbUrxAUHVTrxjSxCWXGmJyIXI+zgaeAu4wxu0TkVmCbMWYL8BcicimQA44CH3fHHhWRz+P80QDcaoyJj49TlFlO2N9F0uvUMmkoCZs2LOfTIUlVNiGkSbKKPRIVnrNwFocli0F8xFDU+NseejEyUS7sWND/tSeQgrKUa/nbsMosNsY8CDxY9N7f+Z7fBNwUMvYu4K4y1qgos5Kgv4vjiZRIoE/AxmGcJKvYI0mLyTBnsb/EBBBaOC5urrgSFVFCJuxYUAZxmEC6ZcuumpWXAC0xoShKCOU4jJPUDvKwFR5Jwj6DCsfZzGVToiJqoy4+BsE1iIqFgEffaLYgwIq1hGqgJSYURQmksznYsRv2vp+k/QXA3kdg61SOMv/E+RlsSlQkodx+BNXMIQDVCBRFCSFsj8rkohvlQLKsYg9b4XHR+sXcvfWNKW6BoIiaWM0hws8QNTbKrg/J7vyD+hGEUa0cAlCNQFGUEMIayo9kJyJDSJNmFXskqQ8UVYI6bi6vzHRUUlrc2KTho2F3/kF5BGEhttXKIQAVBIqihBC18USZKUrJKo6ab4HPFGVTgtojqsxEnNCJiuMvJXw0LIM4KI+g1k1pQAWBoighRG08UWaKUrKKvfmKN26A4UyuoHoDt8wAAA6HSURBVIEkrg8UUmYizs/gxfH7/SFe1FEp1UfDMoiDuprd9tCLfOjc5TXLIQAVBIqihLBpw3JaQrLdFkQ4jEvJKvbma2ua7rb0l4ZIUq46qsyEbeZuUNRRVHP7qOqjUa0ti8tSfH+7Uwrbn21cTVQQKIoSSrpoU/OICnYpJavYI8wv4d3xV7LZTVh5CY8we/+PntofOjYoI9hrUxlWwC5snr++76ma9CIAFQSKokRwLGRjDnsfmFK2wU9UVrFH3B1/JZrdLGhuiCwv4REmSPwx/kFjvcxkrxzFPY/vjQxFjfIr1KprmQoCRVFCSVqOutSIIY+4O/4kPoIwZ7FIcF/hYge4bZRO8dhiU09cSWmbeaqdR6CCQFGUUJJmF5caMeQRd8efxEdQmLjodZg2UyxMguz9YcT1Sw7CW7PtPJpHoCjKjJC0HHWpEUNx4/1hnXGVRz3CnMVh2kyxMAlqbt/aGLxh25TA9uN3TBf7FWzXV0lUECiKEkrSctSlRgx5WN3xx1Qe9YiyvdsIk6B6Q5ncxLSxxdFGYZ8hJWIVDtreVB87R6VRQaAoSiibNiwP22cDy0iUEzEE8T4C6zaVhG/Inc0NVsIkyMSTnTDU18kULaE4qznI1NNQJ3Q0B1f0KfYp9I1mwTjCU/MIFEWZFSRxGJfSh8BPnI8gqbO4eEMWIJufsBImYXONZiemVSX1m8mKTT2dzQ0F34Rt+Gh2wtDSWK95BIqizA6SOIzDTENh7xcTt9GHXSfo7t+z8fvFlQGGM8GO3OK5S40aKmZwLDfNV2ETPtrbNzq78ghE5BIReVFEdovIjQHHPyMiz4nI0yLyMxFZ7TuWF5Gd7iNRKz9FUWaeMI0g0HdQpmkoKvZ/845ehsZy046FOYvB0SQsp542d6lRQ5UMH501eQQikgLuAP4QOAO4UkTOKDptB7DRGHMWcD/wj75jo8aYs93HpRVat6IoNSJsIzNMjxwqJ5kMousN/c8f7ppm0gFobawPNZ3YhlwGCZNSo4aqET46G/IIzgN2G2NeNcZkgHuBy/wnGGMeMcaMuC+3Aisqu0xFUWaKsBBSYFoiVTnJZBBdbygs/j9KyFiHXAbIulKjhsoNHw1jpvMIlgN7fa973PfCuAb4ie91k4hsE5GtIrIpaICIXOues+3QofAORoqi1B7bKqTlJpN5hNUbCiNqs49qjeknyFlcatRQnPAJGlN8PIiZziMICyOefqLI1cBG4Dbf26uMMRuBjwK3i8jJ0y5mzJ3GmI3GmI2LF9v9xymKUhtsq5CWm0wWdE0/IUuI3OyjWmMWU7z+UqOGwsxbHsVjbHwKsyGPoAdY6Xu9AthXfJKIvAv4LHCpMWbce98Ys8/991XgUWBDGetVFGUGsKlCWm4yWdA1/eRDvL5Rm30Sc0rxHXdZUUMxrYj9Y+J8CkGVUSuNjSB4ElgnImtFpBG4ApgS/SMiG4D/jSMEDvre7xKRtPt8EXAh8FylFq8oSm2wqUJabsSQR5hpKMBPDERv9rabedAddzm1horDRaPGxAmroMqolSZWEBhjcsD1wEPA88B9xphdInKriHhRQLcBbcD3isJETwe2ichTwCPAl4wxKggUZY5hE0IaFjEU9n4YYaahsJvsqByFODONd90gm31QHkIYSWsN+cfECatqRwwBBOc8F2GMeRB4sOi9v/M9f1fIuN8AZ5azQEVRZp64ENJNG5ZTJ8F37WFCJIyw08PusWM1jpjpw3oeg10eQlCtoaDyG2Fjbrj4NG564JlI81A1I4ZAM4sVRbEgKoT0li272LyjN9R0EyZEwkgaNRQVPprUTGP7vkeQNhFnUiq2+Qf1Ry6mmhFDoIJAURQLoiJW+kazkaaLKCESRNJNL+r8pGaapGsJ0ibiNvYwm7+/P7KfakcMgQoCRVEsiItYiTKFJN3EwkpfBxGXo2ArVJL0Qi4mTNiEbexBNv+oyKGonINKoYJAURQrkoaBgrNRJ93EokpfFxOXo2ArVJL0Qi4mSNjEhYTa5iwA0/IUqoEKAkVRrLj5A29JPCZh5GiBKHu5nzjhZCtUSvURQLDWEDcuSc5CLaKGVBAoimJFKeaJpBFDHrbDbPzQNkIlSXnrYoK0hrhxxcIjzgSlUUOKosxZkkYMeYQlsBVjU9XURqiELdMmDyFok47b2IuFR5wJSqOGFEWZsySNGPKw1SRsNkibcNQwgRJWDdVPkDYRt7En8RGAffG8UlFBoCiKNUkdxqWGPdpqEjbXD8tU9hMlUOK0k6Clxm3sxcIjTqAlKZ5XCioIFEWxJqnDuNSwRxtNwjYiyUa5iBIocdpJkDYRJ3yKhYf6CBRljiEiHxaRXSIyISIbZ3o9taTa8eweNnf6tt4HG9NQ1OeK006C7ubjhE+x8FAfgaLMPZ4FLgd+MdMLmc3YhoAGYSNwKulHKGeeIKEVJ3yK1xR3x6+ZxYoyyzDGPG+MqW5g9yzG1k9wy6XJ8w6SUCk/Qpw4iZsnSGjFCZ/iNcWdr5nFijKHOR7bsNr6CcrdvOIEjm1EUtw64sRJKZFPccKneE3VvuOPQwWBogQgIj8VkWcDHpcluc7x2IZ104blpOujt45yzEIecQInyeYZlQoQt9GX4kjetGE5jangY0FtP6OEVWkpeclQQaAoARhj3mWMeWvA4wczvbbZwD986KzI45UwC23asJwLT+4OPHbhyd2JNI6Pnr8q8P1UnVjdvYet48rzVwa+D/CPf/S2aQKoTuDvLw/+7q6+IHiNV4W8X0msBIGIXCIiL4rIbhG5MeB4WkS+6x5/XETW+I7d5L7/oohcXLmlK4oyU2zasJzbP3L2tIby6fo6bv/I2RWzad/9ibdz9QWrCnfeKRGuvmAVd3/i7Ymu84VNZ3L1Baum3F23Nqb4vz/8Nqu1hq3jC5vC+25t2rCcL//x2SzvbEZwNI8v/3H4d+OtMckclUJMjCNERFLAS8C7cRrZPwlc6W85KSKfBM4yxlwnIlcAHzTGfEREzgDuAc4DTgR+CpxqjAkty7dx40azbdu2Mj+WooQjItuNMSWHfYrIB4GvAIuBPmCnMSb2Jkd/20o1Ked3baMRnAfsNsa8aozJAPcCxXbSy4Bvus/vB/5ARMR9/15jzLgxZg+w272eosxZjDH/aYxZYYxJG2OW2ggBRZnN2PQsXg7s9b3uAc4PO8cYkxORfmCh+/7WorHT9CIRuRa41n05LiLPWq2+8iwCDs+jeWdy7pn8zDMSorF9+/bDIvJ6yOGZ/D5KQddbXUpZ7+pSJ7MRBEFO62J7Utg5NmMxxtwJ3AkgItvKUdvLYabm1s9c+7lnYl5jTGjY0Ex+H6Wg660utV6vjWmoB/C7xlcA+8LOEZF6YAFw1HKsoiiKMoPYCIIngXUislZEGoErgC1F52wBPuY+/yPg58bxQm8BrnCjitYC64AnKrN0RVEUpRLEmoZcm//1wENACrjLGLNLRG4FthljtgBfB74tIrtxNIEr3LG7ROQ+4DkgB3wqKmLI5c7SP07ZzNTc+pnnz9xhzMY1RaHrrS41XW9s+KiiKIpyfKOZxYqiKPMcFQSKoijznBkTBOWUrajB3J8RkedE5GkR+ZmIlByfm2Re33l/JCKmUk1PbOYVkT92P/MuEfmPSsxrM7eIrBKRR0Rkh/t9v7dC894lIgfDclLE4Z/ddT0tIudUYt5SsP1dzAZEZKX7//W8+1v5y5leUxwiknJ/Xz+a6bXEISKdInK/iLzgfsfJammUijGm5g8cp/MrwElAI/AUcEbROZ8E/s19fgXw3RrOfRHQ4j7/s0rMbTOve147TsOTrcDGGn3edcAOoMt9vaSG3/WdwJ+5z88AXqvQ3L8PnAM8G3L8vcBPcHJdLgAer/TvvJK/i9nyAJYB57jP23HKz8za9brr/AzwH8CPZnotFmv9JvCn7vNGoLMW886URlBO2Yqqz22MecQYM+K+3IqT/1D1eV0+D/wjMFaBOW3n/QRwhzHmGIAx5mAN5zZAh/t8ARXKMzHG/AIngi2My4BvGYetQKeILKvE3Amx/V3MCowx+40xv3WfDwLPE1AtYLYgIiuA9wFfm+m1xCEiHTg3MF8HMMZkjDF9tZh7pgRBUNmK4h/TlLIVgFe2ohZz+7kG586x6vOKyAZgpTGmkiqszec9FThVRH4tIltF5JIazn0LcLWI9AAPAn9eobnjSPo7ON7XkRjXXLsBeHxmVxLJ7cB/ByZmeiEWnAQcAv7dNWV9TURaazHxTAmCcspW1GJu50SRq4GNwG3VnldE6oB/Av66AnNZz+tSj2MeegdwJfA1Eems0dxXAt8wxqzAMdd82/0uqk21fl9JmS3rSISItAHfBz5tjBmY6fUEISLvBw4aY7bP9FosqccxZ/6rMWYDMAzUxGc0U4KgnLIVtZgbEXkX8FngUmPMeA3mbQfeCjwqIq/h2K23VMBhbPtd/8AYkzVOldgXcQRDudjMfQ1wH4Ax5jGgCafgVrWZLeVPZss6rBGRBhwhcLcx5oGZXk8EFwKXun9P9wLvFJHvzOySIukBeowxnoZ1P45gqD4z5BCpB14F1jLpIHtL0TmfYqqz+L4azr0Bx4G3rpafuej8R6mMs9jm814CfNN9vgjHVLGwRnP/BPi4+/x0nE1QKvSdryHcWfw+pjqLn6jU/3U1fxcz/XC/r28Bt8/0WhKu+x3MDWfxL4HT3Oe3ALfVZN4Z/MDvxYk4eAX4rPverTh34ODcGX4Pp4fBE8BJNZz7p8ABYKf72FKLeYvOrYggsPy8AnwZpxTIM8AVNfyuzwB+7W6AO4H3VGjee4D9QBbnTusa4DrgOt9nvsNd1zOV+q4r9R3N1gfwezimq6d9fx/vnel1Wax7rgiCs4Ft7ve7GTeSr9oPLTGhKIoyz9HMYkVRlHmOCgJFUZR5jgoCRVGUeY4KAkVRlHmOCgJFUZR5jgoCRVGUeY4KAkVRlHnO/w9aDSGcQ1IU1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates four polar axes, and accesses them through the returned array\n",
    "# fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))\n",
    "# fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=False))\n",
    "fig, axes = plt.subplots(2, 2)\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "\n",
    "axes[0,0].plot(x,y)\n",
    "axes[1,1].scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 4 Axes>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000A9A1A90>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x000000000A9DAAC8>],\n",
       "        [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000AD10198>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x000000000AD366A0>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Share a X axis with each column of subplots\n",
    "plt.subplots(2,2,sharex='col')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 4 Axes>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000AE9AC50>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x000000000AF063C8>],\n",
       "        [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000AF288D0>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x00000000099D2F60>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Share a Y axis with each row of subplots\n",
    "plt.subplots(2,2,sharey='row')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 4 Axes>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000AD6A1D0>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x0000000009A99AC8>],\n",
       "        [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000009AC1EB8>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x0000000009AF32B0>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Share both X and Y axes with all subplots\n",
    "plt.subplots(2,2,sharex='all',sharey='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
